These course materials cover the course held in 2023, at IT University of Copenhagen, after several iterations of improvements. The materials cover 25 units, each containing a 2-hour lecture plus 2-hour exercise, and additional course materials. Public course page: https://learnit.itu.dk/local/coursebase/view.php?ciid=1218
Prerequisites: Secondary school math. Installed Python environment. No programming skills required.
Ideal level/program: 1st year Bachelor in Data Science
The course is split into two parts:
· 2. Operators, variables, and data types · 3. Lists, functions, and conditionals · 4. Mutability and control flow · 5. Dictionaries · 6. Strings, text, and IO · 7. Comprehensions, shell and scripts · 8. Pandas · 9. Binary search and conda · 10. Web scraping ·
· 11. Array programming with numpy · 12. Single variable analysis · 13. Normal distributions · 14. Data relationships · 15. Simulation and top-down design · 16. Object-oriented programming · 17. Code optimization · 18. Induction and command line tools · 19. Network science · 20. Skewed data · 21. Network analysis and visualization · 22. Graph algorithms · 23. Machine learning · 24. Information theory · 25. Data cleaning and pitfalls ·
admin/: Auxiliary files for the course manager used to create materials, manage the course, or to set up the course page. Not distributed to students.
docs/: Files related to this github repo.
exam/: Materials for creating a written pen&paper exam using the exam LaTeX package. An example mock exam is provided.
files/: General course files to be distributed to students during the course.
mandatory/: Materials for mandatory activities: coding test and home assignments. Not shared publicly.
unit[XX]_[name]/, where [XX] is 01,..,25: The 25 units. All files are distributed students, including contents of files and reading subfolders in advance, except for .key files which are used by the instructor to create the .pdf slides. A few units do not have an exercise. An additional unit 26 is a personalized lecture drawing from the instructor's own research and is not provided here. Additional units 27 and 28 have no materials as they are reserved for taking and discussing a mock exam, respectively.
The course materials were adapted/inspired from a number of sources:
- Scientific Python course by Roberta Sinatra
- A lecture of J.R. Johansson (http://jrjohansson.github.io)
- Python for Data Analysis by Wes McKinney
- Introduction to the practice of statistics by D.S. Moore and G.R. McCabe
- Python Programming by John Zelle
- https://www.thedigitalcatonline.com/blog/2015/03/14/python-3-oop-notebooks/
- https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/classes
- https://www.thedigitalcatonline.com/blog/2015/03/14/python-3-oop-notebooks/
- https://towardsdatascience.com/speed-up-jupyter-notebooks-20716cbe2025
- https://jakevdp.github.io/PythonDataScienceHandbook/01.07-timing-and-profiling.html
- https://people.duke.edu/~ccc14/sta-663/MakingCodeFast.html
- A first course in Network Science by F. Menczer, S. Fortunato, C.A. Davis
- Networks: An Introduction by M.E.J. Newman
- Bruno Gonçalves / Data4Sci: https://github.com/DataForScience/Networks
- James Bagrow: http://bagrow.com/dsv/
- A network science class by Sean Cornelius and Emma Thompson
All materials were used for educational, non-commercial reasons only. Feel free to use as you wish for the same purpose, at your own risk. For other re-use questions please consult the license of the respective source. Our main sources use a CC BY-SA 4.0 license so we use it too.
Python Crash Course: Anastassia Vybornova
Main course organization, and Data Science & Program Design lectures: Michael Szell
Data Science & Program Design exercises: Caroline Bjerre Benn Jørgensen, Jonas-Mika Senghaas